• 한국환경생태학회지
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• 제37권4호
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• pp.289-301
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• 2023

유역 내 산림은 생태계 유지에 있어 중요한 역할을 맡고 있으며 생태네트워크 체계를 구성하는 주요 기반 환경이다. 그러나 지난 수십여 년간 행해진 무분별한 개발사업으로 인해 산림 파편화 및 토지이용 변화가 가속화되었으며 본래의 기능을 상실하게 되었다. 산림 생태계를 파악하는 데 있어 산림의 구조적 패턴은 생태적 과정과 기능에 직접적인 영향을 미치기 때문에 변화패턴을 파악하고 분석하는 것은 중요한 인자라 할 수 있다. 이에 본 연구는 금강 상류 댐 유역을 대상으로 FRAGSTATS 모델을 통해 시계열적인 토지피복변화에 따른 산림 경관의 구조적 변화를 분석하였다. 토지피복 변화탐지를 통한 금강 상류 댐 유역 내 토지피복변화는 1980년대부터 2010년대까지 산림 33.12km²(0.62%), 시가화건조지역 67.26km² (1.26%) 증가하였고 농업지역 148.25km²(2.79%) 감소하였다. 유역 내 산림 경관분석결과 No sampling 분석에서는 경관백분율(PLAND), 면적가중근접지수(CONTIG_AM), 평균 중심지 면적(CORE_MN), 인접지수(PLADJ)가 증가하였고 패치수(NP), 경관형태지수(LSI), 응집지수(COHESION)가 감소하였다. Moving window 분석을 통해 구조적 변화패턴을 파악한 결과, 경상북도 상주시, 충청북도 보은군, 전라북도 진안군 내 산림 경관은 상대적으로 잘 보전되어 있었으나 충청북도 옥천군, 영동군 그리고 충청남도 금산군 사이의 경계부와 전라북도 무주군과 장수군 인접 지역의 산림 경관에서는 파편화가 진행되고 있었다. 결과를 토대로 추후 해당 지역의 산림 관리전략 수립 시 파편화 지역을 대상으로 조림사업을 수립할 필요가 있을 것으로 사료된다. 본 연구를 통해 산림 경관의 파편화가 예상되는 지역을 도출할 수 있었으며, 유역 산림의 건전성 평가 및 관리계획 수립을 위한 기초자료로써 활용될 가능성을 기대할 수 있다.

• 핵의학기술
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• 제18권1호
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• pp.76-81
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• 2014

눈물길 조영검사 시 눈물기관 해부학적 구조의 다양성, 눈물길기형, 검사 중 발생하는 유루증과 같은 경우 전면상 외에 코 안으로의 배출을 좀 더 자세히 표현해낼 수 있으며, 유루증과 코 눈물관으로의 배출을 구별할 수 있는 방법의 필요성을 생각해 보았다. 이에 전면상 지연 촬영 외에 양측 안 와 사위상을 획득하는 방법의 유용성에 대해 알아 보고자 하였다. 2013년 1월부터 2013년 8월까지 눈물길폐쇄로 인한 유루증을 주소로 하는 78명의 환자를 대상으로 하였다. 평균나이는 $56.96{\pm}13.36$세였다. $^{99m}TcO4^-$ 3.7 MBq (0.1 mCi)을 $10{\mu}L$ 씩 마이크로피펫을 이용하여 양 안 하결막낭에 1-2방울 점안 후 1분 씩 20frame, 20분 간 동적 검사를 시행 하였다. 동적 검사 직후 양 안에서 코 안으로 배출이 확인되는 경우는 즉시 사위상을 획득하였고 어느 한쪽의 안 와라도 배출이 보이지 않는 경우 40분 뒤 전면상으로 확인 후 안 와 사 위상을 획득하였다. 검사 시 사용한 장비로는 Pin-hole Collimator를 장착한 Gamma Camera (Siemens Orbiter, Hoffman Estates, IL, USA)장비를 사용하였다. 실험대상 78건의 영상 중 전면상에서 코 안으로의 배출이 확인되었던 환자는 35명이었다. 그 중 양 안 모두에서 코 안으로 배출을 보인 환자는 15명이었고 양 안 8명, 좌 안 2명, 우 안 1명에서 사위상을 통해 보다 나은 배출 양상 관찰이 가능하였다. 좌 안 및 우 안에서 배출이 있었던 환자는 20명이었고 10명이 전면상에 비해 명확한 배출을 보여 주었다. 그 외 배출 가능성이 있는 환자는 13명이었으며 코 눈물관으로의 추적자이동이 보이지 않은 환자는 30명이었다. 기술통계 결과 총 35명 중 21명(60%)이 사위상 추가 촬영으로 전면상보다 뚜렷한 배출 양상을 나타내었다. 배출 감별유용성에 관한 Likert 5점 척도 blind test를 통해 사위상 획득이 전면상에서 자세히 보이지 않는 부분을 나타내 줌으로써 배출 유무 확인이나 배출 지연, 코 눈물관 폐쇄를 진단하는 데 도움이 된다고 응답하였다. 또한 유루증 발생시 코 눈물관으로의 배출과 피부로의 흘림을 구별할 때 사위상이 전면상에 비해 구별능이 높다고 응답하였다(전면상:$4.14{\pm}0.3$, 사위상:$4.55{\pm}0.4$). 눈물길 조영검사 시 양측 안 와 사위상을 전면상 이외에 추가적으로 획득한다면 전면상에서 충분히 보여주지 못하는 영역의 검사가 가능하여 코 눈물관 이후로의 배출 유무를 확인하고 피부에서 유루증의 흐름을 코 눈물관과 구별하는 데 용이하기에 판독의 진단능을 높일 수 있을 것이라 사료된다.

• 지능정보연구
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• 제26권2호
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• pp.131-145
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• 2020

산업혁신의 흐름에 발맞추어 다양한 분야에서 활용되고 있는 IoT 기술은 빅데이터의 접목을 통한 새로운 비즈니스 모델의 창출 및 사용자 친화적 서비스 제공의 핵심적인 요소로 부각되고 있다. 사물인터넷이 적용된 디바이스에서 누적된 데이터는 사용자 환경 및 패턴 분석을 통해 맞춤형 지능 시스템을 제공해줄 수 있어 편의 기반 스마트 시스템 구축에 다방면으로 활용되고 있다. 최근에는 이를 공공영역 혁신에 확대 적용하여 CCTV를 활용한 교통 범죄 문제 해결 등 스마트시티, 스마트 교통 등에 활용하고 있다. 그러나 이미지 데이터를 활용하는 기존 연구에서는 개인에 대한 사생활 침해 문제 및 비(非)일반적 상황에서 객체 감지 성능이 저하되는 한계가 있다. 본 연구에 활용된 IoT 디바이스 기반의 센서 데이터는 개인에 대한 식별이 불필요해 사생활 이슈로부터 자유로운 데이터로, 불특정 다수를 위한 지능형 공공서비스 구축에 효과적으로 활용될 수 있다. 대다수의 국민들이 일상적으로 활용하는 도시철도에서의 지능형 보행자 트래킹 시스템에 IoT 기반의 적외선 센서 디바이스를 활용하고자 하였으며 센서로부터 측정된 온도 데이터를 실시간 송출하고, CNN-LSTM(Convolutional Neural Network-Long Short Term Memory) 알고리즘을 활용하여 구간 내 보행 인원의 수를 예측하고자 하였다. 실험 결과 MLP(Multi-Layer Perceptron) 및 LSTM(Long Short-Term Memory), RNN-LSTM(Recurrent Neural Network-Long Short Term Memory)에 비해 제안한 CNN-LSTM 하이브리드 모형이 가장 우수한 예측성능을 보임을 확인하였다. 본 논문에서 제안한 디바이스 및 모델을 활용하여 그간 개인정보와 관련된 법적 문제로 인해 서비스 제공이 미흡했던 대중교통 내 실시간 모니터링 및 혼잡도 기반의 위기상황 대응 서비스 등 종합적 메트로 서비스를 제공할 수 있을 것으로 기대된다.

• 대한교통학회:학술대회논문집
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• 대한교통학회 1995년도 제27회 학술발표회
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• pp.101-113
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• 1995

The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.